Multiple suppression using a local coherence filter

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Generally, when velocity filters are applied to prestack seismic data, they can suppress long‐period multiples, but they can also deteriorate the primary amplitudes. Since the apparent velocity differences between primaries and multiples can be negligible within the near‐offset regions of data gathers, a velocity filter will be ineffective in these regions, tending to remove or distort primary signal. The normal solution to this problem is to mute or remove near‐offset data, but this approach is detrimental to low‐fold data. In this paper, we derive a filter that can be used to address the problem by responding to average apparent velocity differences rather than to instantaneous apparent velocity differences between primaries and multiples. The filter, called a local coherence filter, is essentially a finite‐difference operator whose step size is equivalent to a spatial prediction distance. Within the local coherence filter’s small application window, an NMO-corrected multiple is predictable while an overcorrected primary is not predictable. The step size of its difference operator gives the filter the ability to discriminate between primaries and long‐period multiples in regions of a data gather where velocity filters fail. This paper derives the local coherence filter and compares it to the f-k filter in both model and real‐data applications. Results demonstrate that the local coherence filter is more effective in suppressing long‐period multiples without distorting the primary reflections.